word2vec+bilstm+crf模型抽取实体关系，输出预测结果代码

下面是一个简单的Python代码示例，用于实现基于word2vec+bilstm+crf的实体关系抽取模型：

import gensim
import numpy as np
import pandas as pd
import tensorflow as tf
from keras.preprocessing.sequence import pad_sequences
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Embedding, Bidirectional, LSTM, Dense, TimeDistributed
from keras_contrib.layers import CRF

# 加载预训练的word2vec模型
w2v_model = gensim.models.KeyedVectors.load_word2vec_format('path/to/word2vec/model')

# 加载训练数据
train_data = pd.read_csv('path/to/train/data')

# 将文本转换为词向量序列
X_train = []
for sentence in train_data['text']:
    words = sentence.split()
    word_vectors = []
    for word in words:
        if word in w2v_model:
            word_vectors.append(w2v_model[word])
        else:
            word_vectors.append(np.zeros(w2v_model.vector_size))
    X_train.append(word_vectors)

# 对词向量序列进行填充，使其长度相同
X_train = pad_sequences(X_train, padding='post', maxlen=100)

# 将标签转换为one-hot编码
y_train = to_categorical(train_data['label'].values)

# 构建模型
model = Sequential()
model.add(Embedding(input_dim=len(w2v_model.vocab), output_dim=w2v_model.vector_size, weights=[w2v_model.vectors]))
model.add(Bidirectional(LSTM(units=128, return_sequences=True)))
model.add(TimeDistributed(Dense(units=128, activation='relu')))
crf = CRF(units=len(y_train[0]), sparse_target=True)
model.add(crf)

# 编译模型
model.compile(optimizer='adam', loss=crf.loss_function, metrics=[crf.accuracy])

# 训练模型
model.fit(X_train, y_train, epochs=10, batch_size=32)

# 加载测试数据
test_data = pd.read_csv('path/to/test/data')

# 将测试数据转换为词向量序列
X_test = []
for sentence in test_data['text']:
    words = sentence.split()
    word_vectors = []
    for word in words:
        if word in w2v_model:
            word_vectors.append(w2v_model[word])
        else:
            word_vectors.append(np.zeros(w2v_model.vector_size))
    X_test.append(word_vectors)

# 对词向量序列进行填充，使其长度相同
X_test = pad_sequences(X_test, padding='post', maxlen=100)

# 预测测试数据
y_pred = model.predict(X_test)

# 将预测结果转换为标签
y_pred_labels = []
for i in range(len(y_pred)):
    pred_labels = []
    for j in range(len(y_pred[i])):
        pred_labels.append(np.argmax(y_pred[i][j]))
    y_pred_labels.append(pred_labels)

# 输出预测结果
for i in range(len(test_data)):
    print(test_data['text'][i])
    print('Predicted labels:', y_pred_labels[i])

需要注意的是，上述代码仅供参考，具体实现细节还需要根据实际情况进行调整。同时，该模型的训练和预测需要大量的计算资源和时间，在实际应用中需要进行优化。